Fuzzy control-based wind turbine generator adaptive control method, device and equipment

Abstract

The invention discloses a wind turbine generator self-adaptive control method, device and equipment based on fuzzy control. The method comprises the following steps: step S01, obtaining operation parameters and environment parameters in the operation process of a controlled unit; s02, estimating the current wind speed according to the operation parameters and the environment parameters in the operation process of the controlled unit; s03, calculating real-time turbulence intensity according to the estimated current wind speed; step S04, judging whether the real-time turbulence intensity reaches a preset turbulence condition or not; and S05, if the real-time turbulence intensity reaches the preset turbulence condition, performing power reduction scheduling based on fuzzy control according to the real-time turbulence intensity so as to control the controlled unit to operate in a power reduction manner according to the output power obtained by the fuzzy control. On the premise of not increasing the limit load of the blade and the hub of the wind generating set, the running stability of the set in a complex and large turbulence terrain environment can be improved, the safe running of the set is ensured, and the generating capacity loss of the set is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind turbine control, and particularly relates to a wind turbine adaptive control method, device and equipment based on fuzzy control. BACKGROUND

[0002] The terrain environment where the current wind turbine is located is relatively complex, the turbulence intensity of some sites is large, the wind speed changes fast, and the wind direction changes frequently. In such an environment, the limit load of the wind turbine hub, blade, tower drum and the like is prone to over-limit, so that the vibration over-limit fault is frequently triggered during operation to trigger shutdown, so as to ensure that the limit load reduces the power generation loss and improves the operation stability of the unit.

[0003] In order to improve the operation stability of the wind turbine in the complex terrain with large turbulence, the following methods are usually used in the prior art: First: control method based on vibration sensor monitoring This kind of method is to install a vibration sensor on the nacelle to monitor the vibration state during the operation of the whole machine. When the vibration sensor detects an abnormal state, the unit is controlled and protected. However, this method needs to add a vibration sensor, which will increase the implementation cost. On the other hand, the vibration sensor is difficult to accurately monitor the vibration state of the position such as the inside of the blade and the lower part of the tower drum, so it is difficult to fully reflect the vibration state of the whole wind turbine, and the vibration sensor is also easily disturbed by external factors, resulting in low actual control accuracy and poor robustness. Second: control method based on laser radar monitoring This kind of method is to install a laser radar to monitor the wind speed and turbulence intensity, and to control the wind turbine to stop when the turbulence intensity exceeds a certain threshold. However, the laser radar device is expensive, which will greatly increase the implementation cost. In addition, this kind of method is to stop protection when the turbulence intensity exceeds the threshold, while the influence of turbulence intensity of different degrees on the operation safety of the wind turbine is different. For example, the influence of slight turbulence intensity on the operation safety of the unit is small. This kind of method will affect the operation efficiency of the unit and cause large power generation loss.

[0004] In summary, how to realize adaptive control of the wind turbine based on real-time environmental turbulence state so as to improve the operation stability of the unit in complex terrain has become a problem to be solved at present. SUMMARY

[0005] The technical problem to be solved by the present invention is as follows: In view of the above-mentioned technical problems existing in the prior art, the present invention provides a simple, low-cost, high-precision, and robust adaptive control method, device and equipment for wind turbines based on fuzzy control. It can improve the operating stability of the wind turbine in complex and highly turbulent terrain environments without increasing the ultimate load of the wind turbine blades and hub, thus ensuring the safe operation of the unit and reducing the loss of power generation.

[0006] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows: An adaptive control method for wind turbine generators based on fuzzy control, comprising the following steps: Step S01. Obtain the operating parameters of the controlled unit during operation and the environmental parameters of the environment in which the controlled unit is located; Step S02. Estimate the current wind speed of the environment in which the controlled unit is located based on the operating parameters and environmental parameters during the operation of the controlled unit; Step S03. Calculate the real-time turbulence intensity of the environment in which the controlled unit is located based on the estimated current wind speed; Step S04. Determine whether the real-time turbulence intensity has reached the preset turbulence condition; Step S05. If the real-time turbulence intensity reaches the preset turbulence condition, power reduction scheduling is performed based on fuzzy control according to the real-time turbulence intensity, so as to control the controlled unit to operate at reduced power according to the output power obtained by fuzzy control.

[0007] Further, in step S05, the power reduction scheduling based on fuzzy control according to the real-time turbulence intensity includes: The real-time turbulence intensity is input to the fuzzy controller; The real-time turbulence intensity is fuzzified by a fuzzy controller, which maps the real-time turbulence intensity to multiple quantization levels, and the output power of the wind turbine is also mapped to multiple quantization levels. The fuzzy controller performs fuzzy inference on the fuzzified result based on fuzzy rules, and after defuzzifying the result obtained by fuzzy inference, it obtains the output power corresponding to the current real-time turbulence intensity, generates the corresponding fuzzy control output quantity, and superimposes it on the operating control quantity of the controlled unit for power reduction scheduling.

[0008] Furthermore, the fuzzy rules include determining the turbulence intensity level if the current state is an ideal state with zero power error; maintaining the current control if the real-time turbulence intensity is normal; otherwise, reducing the output power. Different levels of output power reduction control are applied based on the intensity level of the real-time turbulence. Specifically, if the real-time turbulence intensity exceeds the normal state but is less than a preset turbulence intensity threshold, the output power is reduced by a first-level reduction amount; if the real-time turbulence intensity exceeds the preset turbulence intensity threshold, the output power is reduced by a second-level reduction amount; if the current power deviation is within the range of 0 to a preset power deviation threshold, the output power is reduced by a first-level reduction amount; if the current power deviation exceeds the preset power deviation threshold, the output power is reduced by a second-level reduction amount. The first-level reduction amount is less than the second-level reduction amount.

[0009] Further, in step S04, if it is determined that the real-time turbulence intensity exceeds the first preset threshold, then proceed to step S05; if it is determined that the real-time turbulence intensity exceeds the second preset threshold, then control the shutdown action, wherein the second preset threshold is greater than the first preset threshold.

[0010] Furthermore, the operating parameters include generator speed and blade pitch angle. Step S01 also includes obtaining the inherent parameters of the controlled wind turbine, including the current turbine rotor diameter, rotor tilt angle, generator moment of inertia, and transmission chain efficiency.

[0011] Furthermore, the formula for estimating the current wind speed is:

[0012] Where P is power, A is the rotor area, v is wind speed, ρ is air density, and Cp is power coefficient.

[0013] Further, in step S03, the standard deviation and average value of the sliding window are calculated based on the estimated wind speed data to obtain the real-time turbulence intensity of the environment in which the controlled unit is located.

[0014] An adaptive control device for wind turbine generators based on fuzzy control, comprising: The parameter acquisition module is used to acquire the operating parameters of the controlled unit during operation and the environmental parameters of the environment in which the controlled unit is located. The wind speed estimation module is used to estimate the current wind speed of the environment in which the controlled unit is located based on the operating parameters and environmental parameters during the operation of the controlled unit. The environmental turbulence assessment module is used to estimate the real-time turbulence intensity of the environment in which the computer group is located based on the current wind speed. The turbulence state determination module is used to determine whether the real-time turbulence intensity has reached the preset turbulence condition; An adaptive control module is used to perform power reduction scheduling based on fuzzy control according to the real-time turbulence intensity if the real-time turbulence intensity reaches the preset turbulence condition, so as to control the controlled unit to operate at reduced power according to the output power obtained by fuzzy control.

[0015] An electronic device includes a processor and a memory, the memory being used to store a computer program, and the processor being used to execute the computer program to perform the method described above.

[0016] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention monitors the operating parameters and environmental parameters during the operation of the wind turbine, estimates the current wind speed of the environment in which the controlled wind turbine is located in real time, and then calculates the real-time turbulence intensity based on the estimated wind speed. The operating mode of the wind turbine is controlled according to the state of the real-time turbulence intensity. It can achieve adaptive control of the wind turbine's operating state based on the real-time state of environmental turbulence. At the same time, when the real-time turbulence intensity reaches the preset turbulence conditions, it combines fuzzy control to perform power reduction scheduling control. It can improve the operating stability of the wind turbine in complex and highly turbulent terrain environments without increasing the ultimate load of the wind turbine blades and hub, ensuring safe operation of the unit, reducing the loss of power generation, and without relying on additional hardware equipment, which can reduce the implementation cost and complexity.

[0018] 2. This invention employs fuzzy control to reduce power based on real-time turbulence intensity when the real-time turbulence intensity reaches a high turbulence state. This allows for precise determination of the required output power of the unit under different turbulence intensity states and wind resource environments. Simultaneously, it enhances the robustness of the control process against external disturbances, thereby ensuring the safe operation of the unit under high turbulence and harsh environmental resource conditions. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the implementation process of the adaptive control method for wind turbine generators based on fuzzy control in this embodiment.

[0020] Figure 2 This is a schematic diagram illustrating the principle of wind speed estimation in this embodiment.

[0021] Figure 3 This is a schematic diagram illustrating the implementation process of turbulence intensity state assessment and control logic in this embodiment.

[0022] Figure 4 This is a schematic diagram illustrating the principle of using a fuzzy controller to perform power reduction scheduling control of the generator unit in this embodiment. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0024] As disclosed in this invention, unless the context clearly indicates otherwise, words such as "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. The terms "first," "second," and similar terms used in this invention disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, words such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0025] like Figure 1 As shown, the steps of the wind turbine adaptive control method based on fuzzy control in this embodiment include: Step S01. Obtain the operating parameters of the controlled unit during operation and the environmental parameters of the environment in which the controlled unit is located.

[0026] In this embodiment, the operating parameters include output power, real-time power factor, etc., and the environmental parameters include air density, etc., which can be configured according to the parameters required to estimate the wind speed.

[0027] In a specific application embodiment, during the normal operation of the wind turbine generator set, data such as the generator set's output power and real-time power coefficient are collected, and the collected data is subjected to sliding window filtering and data cleaning to obtain filtered operating parameter data such as generator speed and pitch angle, which can further ensure data reliability and eliminate signal interference.

[0028] Furthermore, it also includes acquiring the inherent parameters of the controlled wind turbine, including the rotor area, etc., for subsequent real-time wind speed estimation based on the operating parameters and inherent parameters during the operation of the controlled unit.

[0029] Step S02. Estimate the current wind speed of the environment in which the controlled unit is located based on the operating parameters and environmental parameters during the operation of the controlled unit.

[0030] In this embodiment, the current wind speed of the environment in which the controlled unit is located is obtained by real-time wind speed estimation based on the operating parameters during the operation of the controlled unit and the inherent parameters of the unit (such as rotor area). Figure 2As shown, by real-time monitoring and filtering of the generator speed during the operation of the controlled unit, as well as monitoring the air density in the environment where the controlled unit is located and obtaining inherent parameters such as the wind turbine area of ​​the unit, the current wind speed is estimated using the above parameters. W EST .

[0031] For example, the formula for estimating the current wind speed can be expressed as: (1) Where P is power, A is the rotor area, v is wind speed, ρ is air density, and Cp is power coefficient.

[0032] Using the above formula (1), the current wind speed can be accurately estimated based on the operating status (output power, real-time power coefficient), inherent parameters (wind turbine area), and environmental conditions (air density) of the controlled unit.

[0033] Step S03. Based on the estimated current wind speed, the computer group determines the real-time turbulence intensity of the environment.

[0034] In this embodiment, the standard deviation and average value of the sliding window are calculated based on the estimated wind speed data to obtain the real-time turbulence intensity of the environment in which the controlled unit is located.

[0035] Specifically, the sliding window mean (moving average) can smooth the data and reduce noise, while the sliding window standard deviation can measure the dispersion of the data within the sliding window. In this embodiment, after calculating the sliding window standard deviation and the sliding window mean from the estimated wind speed data, the real-time turbulence intensity is obtained by taking the ratio of the sliding window standard deviation to the sliding window mean, i.e.: (2) in, TI i This represents the turbulence intensity at time i.

[0036] Understandably, in addition to the methods mentioned above, other methods can also be used to calculate the real-time turbulence intensity based on the estimated wind speed.

[0037] Step S04. Determine whether the real-time turbulence intensity has reached the preset turbulence conditions.

[0038] In this embodiment, a turbulence intensity threshold is preset. If the real-time turbulence intensity exceeds the turbulence intensity threshold, it is determined to be in a state of high turbulence that needs to be controlled; otherwise, it is determined to be in a normal turbulence state.

[0039] Furthermore, it also includes: if it is determined that the real-time turbulence intensity exceeds the first preset threshold, then proceed to step S05; if it is determined that the real-time turbulence intensity exceeds the second preset threshold, then control the shutdown action, wherein the second preset threshold is greater than the first preset threshold.

[0040] like Figure 3 As shown, a first preset threshold Tur1 and a second preset threshold Tur2 are preset. The real-time turbulence intensity Tur is first compared with the first preset threshold Tur1. If the real-time turbulence intensity Tur > Tur1, it indicates that the real-time turbulence intensity is large. Then, the real-time turbulence intensity Tur is further compared with the second preset threshold Tur2. Otherwise, the current normal operation control mode remains unchanged. When comparing the real-time turbulence intensity Tur with the second preset threshold Tur2, if Tur > Tur2, that is, the real-time turbulence intensity is too large, the control protection shutdown is performed. Otherwise, step S05 is entered to perform power reduction operation.

[0041] Step S05. If the real-time turbulence intensity reaches the preset turbulence condition, power reduction scheduling is performed based on fuzzy control according to the real-time turbulence intensity, so as to control the controlled unit to operate at reduced power according to the output power obtained by fuzzy control.

[0042] In this embodiment, if the real-time turbulence intensity is assessed to reach a state of high turbulence, a fuzzy control method is used to perform power reduction scheduling based on the real-time turbulence intensity. This can accurately determine the output power required by the unit under different turbulence intensity states and wind resource environments. At the same time, it can improve the robustness of the control process to external disturbances and ensure the safe operation of the unit under high turbulence and harsh environmental resource conditions.

[0043] In this embodiment, the specific steps for power reduction scheduling based on fuzzy control according to the current wind speed and real-time turbulence intensity include: The temporal turbulence intensity is input into the fuzzy controller; The real-time turbulence intensity is fuzzified by a fuzzy controller, which maps the real-time turbulence intensity to multiple quantization levels, and the output power of the wind turbine is also mapped to multiple quantization levels. The fuzzy controller performs fuzzy inference on the fuzzified result based on fuzzy rules, and after defuzzifying the result obtained by fuzzy inference, it obtains the output power corresponding to the current real-time turbulence intensity, generates the corresponding fuzzy control output quantity, and superimposes it on the operating control quantity of the controlled unit for power reduction scheduling.

[0044] In this embodiment, the power reduction scheduling is determined by a fuzzy controller. The fuzzy rules specifically include: if the current state is an ideal state ZEp with zero power error, the turbulence intensity level is determined. If the real-time turbulence intensity is in a normal state ZEt (no turbulence or very weak turbulence), the current control is maintained; otherwise, the output power is reduced. Different levels of output power reduction control are performed according to the intensity level of the real-time turbulence. Specifically, if the real-time turbulence intensity exceeds the normal state but is less than the preset turbulence intensity threshold, i.e., the real-time turbulence intensity is in a mild turbulence state, the output power is reduced according to the first level of reduction. If the real-time turbulence intensity exceeds the preset turbulence intensity threshold, i.e., the real-time turbulence intensity is in a moderate or higher turbulence state, the output power is reduced according to the second level of reduction. If the current power deviation is within the range of 0 to the preset power deviation threshold, i.e., the current power deviation is a mild power deviation, the output power is reduced according to the first level of reduction. If the current power deviation exceeds the preset power deviation threshold, i.e., the current power deviation is a moderate or higher power deviation, the output power is reduced according to the second level of reduction. The first level of reduction is less than the second level of reduction.

[0045] In specific application embodiments, such as Figure 4 As shown, the estimated current wind speed and calculated real-time turbulence intensity are input into the fuzzy controller. After fuzzification, fuzzy inference is performed according to fuzzy rules. The fuzzy controller then calculates the real-time environmental turbulence intensity. Tur Fuzzification processing is performed, with specific fuzzy rules set as shown in Table 1. The real-time environmental turbulence intensity is mapped to multiple quantization levels, namely ZEt, PSt, PMt, and PBt, with ZEt, PSt, PMt, and PBt increasing in strength sequentially. The fuzzy controller performs fuzzification processing on the power, mapping it to multiple quantization levels, namely ZEp, PSt, PMp, and PBp, with ZEp, PSt, PMp, and PBp increasing in strength sequentially. Finally, the results obtained from fuzzy inference are defuzzified to obtain the final fuzzy control output, which is superimposed on the current unit operation control output to set the power and speed setpoints for power reduction and speed reduction scheduling. In Table 1, ZEt represents the normal state with no or very weak turbulence, PST represents the state with slight turbulence, PMt represents the state with moderate turbulence, PBt represents the state with severe turbulence, Zep represents the ideal state with zero power error, PSP represents the state with slight power error, PMp represents the state with moderate power error, PBp represents the state with severe power error, PO represents maintaining the current control, NS represents the state with slight power reduction, NM represents the state with moderate power reduction, and NB represents the state with significant power reduction. The power reduction amounts are listed in descending order as NS. <NM<NB。

[0046] Table 1: Fuzzy Rule Table

[0047] This embodiment monitors the operating parameters of the wind turbine during operation, estimates the current wind speed of the environment in which the controlled wind turbine is located in real time, and then calculates the real-time turbulence intensity based on the estimated wind speed. The operating mode of the wind turbine is controlled according to the state of the real-time turbulence intensity. Adaptive control of the wind turbine's operating state can be achieved based on the real-time state of environmental turbulence. At the same time, when the real-time turbulence intensity reaches the preset turbulence conditions, fuzzy control is used to reduce power dispatch. This can improve the operating stability of the wind turbine in complex and highly turbulent terrain environments without increasing the ultimate load on the wind turbine blades and hub, ensuring safe operation of the unit, reducing power generation loss, and without relying on additional hardware equipment, thus reducing implementation costs and complexity.

[0048] The following example, using the method of the present invention to achieve adaptive control of wind turbine generators in a specific application embodiment, further illustrates the present invention. The detailed steps are as follows: Step 1: During the normal operation of the wind turbine generator set, collect the generator speed and blade pitch angle, and perform data cleaning.

[0049] Step 2: Filter and delay the pitch angle and generator speed data obtained in Step 1, and estimate the wind speed W based on the inherent parameters such as the rotor diameter, rotor tilt angle, generator moment of inertia, and transmission chain efficiency. EST To ensure data reliability, the estimated wind speed W was further calculated. EST The wind speed was cross-compared with that obtained from the unit's mechanical components and the ultrasonic anemometer. If the estimated wind speed W... EST If there is a significant deviation between the wind speed obtained from the unit's mechanical and ultrasonic anemometers, the wind speeds obtained from the two closest methods will be used to obtain the final estimated wind speed W. EST .

[0050] Step 3: Calculate the current estimated wind speed W obtained in Step 2. EST Calculate its 200s sliding window standard deviation and mean, and combine it with the turbulence calculation formula to calculate the real-time turbulence Tur of the unit based on the estimated wind speed; Step 4: Set two turbulence intensity thresholds, Tur1 and Tur2; input the real-time turbulence intensity calculated in Step 3 into the interpolation table. If the current turbulence intensity Tur is greater than the control threshold Tur1, proceed to Step 5. If the current turbulence intensity is greater than the safety threshold Tur2, execute the shutdown action.

[0051] Step 5. Switch the unit to reduced power operation mode. The specific power reduction scheduling is decided by the fuzzy controller based on the current real-time turbulence intensity.

[0052] In this embodiment, the wind turbine adaptive control device based on fuzzy control includes: The parameter acquisition module is used to acquire the operating parameters of the controlled unit during operation and the environmental parameters of the environment in which the controlled unit is located. The wind speed estimation module is used to estimate the current wind speed of the environment in which the controlled unit is located based on the operating parameters and environmental parameters during the operation of the controlled unit. The environmental turbulence assessment module is used to estimate the real-time turbulence intensity of the environment in which the computer group is located based on the current wind speed. The turbulence state determination module is used to determine whether the real-time turbulence intensity has reached the preset turbulence condition; The adaptive control module is used to reduce power scheduling based on fuzzy control according to the real-time turbulence intensity if the real-time turbulence intensity reaches the preset turbulence conditions, so as to control the controlled unit to operate at reduced power according to the output power obtained by fuzzy control.

[0053] In this embodiment, in the turbulence state judgment module, if it is determined that the real-time turbulence intensity exceeds the first preset threshold Tur1, it switches to the adaptive control module; if it is determined that the real-time turbulence intensity exceeds the second preset threshold Tur2, it controls the shutdown action. The second preset threshold Tur2 is greater than the first preset threshold Tur1.

[0054] The wind turbine adaptive control device based on fuzzy control in this embodiment corresponds one-to-one with the wind turbine adaptive control method based on fuzzy control described above, and will not be described in detail here.

[0055] This embodiment further provides an electronic device, including a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to perform the method as described above.

[0056] It is understood that the method described in this embodiment can be executed by a single device, such as a computer or server, or it can be applied to a distributed scenario where multiple devices cooperate to complete the task. In a distributed scenario, one of the multiple devices may execute only one or more steps of the method described in this embodiment, and the multiple devices interact to complete the method. The processor can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit, or one or more integrated circuits, and is used to execute relevant programs to implement the method described in this embodiment. The memory can be implemented using read-only memory (ROM), random access memory (RAM), static storage devices, and dynamic storage devices. The memory can store the operating system and other applications. When the method described in this embodiment is implemented through software or firmware, the relevant program code is stored in the memory and called and executed by the processor.

[0057] This embodiment further provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0058] Those skilled in the art will understand that the above embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce implementations of the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0059] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A fuzzy control based adaptive control method for wind turbine generators, characterized by the steps of Comprising: Step S01. acquiring operating parameters in the process of operation of the controlled unit and environmental parameters in the environment where the controlled unit is located; Step S02. estimating the current wind speed of the environment where the controlled unit is located according to the operating parameters and the environmental parameters in the process of operation of the controlled unit; Step S03. calculating the real-time turbulence intensity of the environment where the controlled unit is located according to the estimated current wind speed; Step S04. judging whether the real-time turbulence intensity reaches a preset turbulence condition; Step S05. if the real-time turbulence intensity reaches the preset turbulence condition, performing power reduction scheduling based on fuzzy control according to the real-time turbulence intensity, so as to control the controlled unit to operate at a reduced power according to the output power obtained by fuzzy control.

2. The fuzzy control based adaptive control method of a wind turbine generator unit according to claim 1, wherein, In step S05, the power reduction scheduling based on fuzzy control according to the real-time turbulence intensity comprises: inputting the real-time turbulence intensity into a fuzzy controller; performing fuzzy processing on the real-time turbulence intensity by the fuzzy controller, mapping the real-time turbulence intensity to a plurality of quantization levels, and mapping the output power of the wind turbine to the plurality of quantization levels; performing fuzzy reasoning on the results after the fuzzy processing by the fuzzy controller, obtaining the output power corresponding to the current real-time turbulence intensity after de-fuzzification of the results obtained by the fuzzy reasoning, generating a corresponding fuzzy control output and superimposing it into the controlled unit operation control quantity for power reduction scheduling.

3. The fuzzy control based adaptive control method of a wind turbine generator unit according to claim 2, wherein, The fuzzy rule comprises judging the turbulence intensity level if the current is in the ideal state of zero power error, keeping the current control if the real-time turbulence intensity is in the normal state, otherwise controlling the output power to be reduced, and performing output power reduction control of different levels according to the intensity level of the real-time turbulence intensity, wherein if the real-time turbulence intensity exceeds the normal state but is less than a preset turbulence intensity threshold, the output power is reduced according to a first level reduction control, if the real-time turbulence intensity exceeds the preset turbulence intensity threshold, the output power is reduced according to a second level reduction control, if the current power deviation is within the range of 0 to a preset power deviation threshold, the output power is reduced according to the first level reduction control, and if the current power deviation exceeds the preset power deviation threshold, the output power is reduced according to the second level reduction control, the first level reduction control being less than the second level reduction control.

4. The fuzzy control based adaptive control method of a wind turbine generator unit according to claim 1, wherein In step S04, if it is judged that the real-time turbulence intensity exceeds the first preset threshold, step S05 is entered, and if it is judged that the real-time turbulence intensity exceeds the second preset threshold, a shutdown action is controlled, the second preset threshold being greater than the first preset threshold.

5. The fuzzy control based adaptive control method of a wind turbine generator unit according to claim 1, wherein, The operating parameters comprise generator speed and pitch angle, and step S01 further comprises acquiring inherent parameters of the controlled wind turbine, the inherent parameters comprising current unit wind wheel diameter, wind wheel inclination angle, generator moment of inertia and transmission chain efficiency.

6. The fuzzy control based adaptive control method of a wind turbine generator unit according to claim 5, wherein, The calculation expression for estimating the current wind speed is: wherein P is power, A is wind wheel area, v is wind speed, p is air density, and Cp is power coefficient.

7. The fuzzy control based adaptive control method of wind turbine generator according to any one of claims 1 to 6, characterized in that, In step S03, the sliding window standard deviation and the sliding window average value are calculated according to the estimated wind speed data, and the real-time turbulence intensity of the environment where the controlled unit is located is obtained.

8. A wind turbine generator adaptive control device based on fuzzy control, characterized by, Comprising: a parameter acquisition module, configured to acquire an operating parameter in an operating process of the controlled unit and an environmental parameter in an environment where the controlled unit is located; a wind speed estimation module, configured to estimate a current wind speed of the environment where the controlled unit is located according to the operating parameter in the operating process of the controlled unit and the environmental parameter; an environmental turbulence evaluation module, configured to calculate a real-time turbulence intensity of the environment where the controlled unit is located according to the estimated current wind speed; a turbulence state judgment module, configured to judge whether the real-time turbulence intensity reaches a preset turbulence condition; an adaptive control module, configured to, if the real-time turbulence intensity reaches the preset turbulence condition, perform power reduction scheduling based on fuzzy control according to the real-time turbulence intensity, so as to control the controlled unit to operate in a power reduction mode according to an output power obtained by the fuzzy control.

9. An electronic device comprising a processor and a memory for storing a computer program, characterized in that The processor is configured to execute the computer program to perform the method of any one of claims 1-7.

10. A computer readable storage medium storing a computer program, characterized in that, The computer program, when executed by the processor, implements the method of any one of claims 1-7.

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